In [1]:
%matplotlib notebook
%reload_ext autoreload
%autoreload 2
import sys
import os
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
plt.rcParams.update({'figure.max_open_warning': 0})
np.random.seed(123456)
Function to generate $\mathbf{R}$¶
In [2]:
import numpy as np
sys.path.append('../../')
from vis.generators import dtlz2
from vis.generators import c2dtlz2
from vis.generators import c0dtlz2
def generate_r(F, frontname, verbose=False):
n = {'dtlz2': F.shape[0], 'c2dtlz2': 210000, 'c0dtlz2': 5000}
LB, UB = np.mean(np.min(F, axis=0)), np.mean(np.max(F, axis=0))
r = UB - LB
if frontname == 'dtlz2':
R, _ = dtlz2.surface(r=r, n=n[frontname], m=F.shape[1], mode='lhcl2')
elif frontname == 'c2dtlz2':
R, _, _ = c2dtlz2.surface(r=r, n=n[frontname], m=F.shape[1], mode='lhcl2')
elif frontname == 'c0dtlz2':
R, _, _ = c0dtlz2.surface(r=r, n=n[frontname], m=F.shape[1])
else:
raise ValueError("PF generation function for {:s} is not supported.".format(frontname))
# check
if verbose:
print("F.shape =", F.shape)
print("R.shape =", R.shape)
return R
An L2-norm based $\mathcal{d}_{\mathbf{F}}(\cdot)$ approximation function¶
In [3]:
import os
import numpy as np
sys.path.append('../../')
from vis.tda import simple_shape
from vis.utils import io
def approximate_depth_l2norm(F, frontname, save_ref_depth=False, verbose=False):
# Generate a random data points that are on the same surface as F.
# These points are R
R = generate_r(F, frontname, verbose=verbose)
# Now find depth contours from R
if verbose:
print("Computing depth contours of", pf)
Dr = simple_shape.depth_contours(R, verbose=verbose)
# Save in a temp file
if save_ref_depth:
drfname = frontname + "-r-depth-cont-cvhull.csv"
drfpath = os.path.join("/tmp", drfname)
io.savetxt(drfpath, Dr, fmt='{:d}', delimiter=',')
# Find depth contour of F from that of R
Dr_ = Dr[::-1]
Id = np.arange(0,F.shape[0]).astype(int)
Df = []
while Id.shape[0] > 0:
# for each layer l in d_R
for l in Dr_:
s = []
# for each index i in l
for i in l.astype(int):
# find the point j in F that is closest to R[i]
if Id.shape[0] > 0:
j = np.argmin(np.linalg.norm(F[Id]-R[i], axis=1))
s.append(Id[j])
Id = np.delete(Id, j)
else:
break
if len(s) > 0:
Df.append(np.array(s).astype(int))
Df = np.array(Df, dtype=object)
Df = Df[::-1]
# print and check counts
if verbose:
print("Df.shape[0] =", Df.shape[0])
s = 0
for i,l in enumerate(Df):
print("Df[{:d}].shape[0] =".format(i), l.shape[0])
s = s + l.shape[0]
print("total =", s)
print("Dr.shape[0] =", Dr.shape[0])
s = 0
for i,l in enumerate(Dr):
print("Dr[{:d}].shape[0] =".format(i), l.shape[0])
s = s + l.shape[0]
print("total =", s)
return R,Dr,Df
Find depth-contours for the failed case¶
In [4]:
sys.path.append('../../')
from vis.utils import io
from vis.plotting import paletteviz
# Data where the depth contours were not
# possible to compute, these are F's.
pf, dim = 'dtlz2-nbi', '5d'
fpath = "../../data/{0:s}/{1:s}/dataf.csv".format(pf, dim)
if os.path.exists(fpath):
path, _ = os.path.split(fpath)
dirs = path.split('/')
frontname = dirs[-2].split('-')[0]
F = np.loadtxt(fpath, delimiter=',')
# Approximate depth contours of F from R
R,Dr,Df = approximate_depth_l2norm(F, frontname, verbose=True, save_ref_depth=True)
# plot and verify
paletteviz.plot(R, ax=Axes3D(plt.figure()), depth_contours=Dr, mode='star', \
n_partitions=4, s=1, c='blue', verbose=True, title="PaletteViz of R")
plt.show()
# plot and verify
paletteviz.plot(F, ax=Axes3D(plt.figure()), depth_contours=Df, mode='star', \
n_partitions=4, s=1, c='blue', verbose=True, title="PaletteViz of F")
plt.show()
# save the layers
io.savetxt(os.path.join(path, "depth-cont-cvhull.csv"), Df, fmt='{:d}', delimiter=',')
Compute trade-offs¶
In [5]:
%matplotlib notebook
sys.path.append('../../')
from vis.utils import dm
from vis.plotting.utils import resize_by_tradeoff, default_color, \
color_by_cv, enhance_color
eps = dm.epsilons
pf,dim = 'dtlz2-nbi', '5d'
fullpathf = "../../data/{0:s}/{1:s}/dataf.csv".format(pf, dim)
if os.path.exists(fullpathf):
path, filenamef = os.path.split(fullpathf)
dirs = path.split('/')
frontname = dirs[-2]
F = np.loadtxt(fullpathf, delimiter=',')
print(fullpathf, F.shape, dirs, frontname)
Mu,Ik = dm.tradeoff(F, epsilon=eps[dim], penalize_extremes=False)
mupathf = os.path.join(path, "mu.csv")
np.savetxt(mupathf, Mu, delimiter=',', fmt="%1.4e")
muidxpathf = os.path.join(path, "muid.csv")
np.savetxt(muidxpathf, Ik, delimiter=',', fmt="%d")
# default color array
C = default_color(F.shape[0], alpha=0.5)
# change the size w.r.t. Mu, the values are
# too big for smaller size plots, so we make
# them half.
S = resize_by_tradeoff(Mu, k=Ik)
# load CV if exists, use it for coloring
cvpathf = os.path.join(path, "datacv.csv")
if os.path.exists(cvpathf):
CV = np.loadtxt(cvpathf, delimiter=',')
C = color_by_cv(CV)
# enhance the knees with red
C = enhance_color(C, k=Ik)
# plot
ax = Axes3D(plt.figure())
ax.scatter(F[:,0], F[:,1], F[:,2], c=C, s=S)
plt.show()
else:
print("Error: {:s} not found.".format(fullpathf))
Get appropriate color codings and other markings¶
In [6]:
sys.path.append('../../')
from vis.plotting.utils import resize_by_tradeoff, color_by_dist, color_by_cv, enhance_color
def load_props(path):
r"""
Load CV, Mu, Ik and S from path.
"""
CV = None
cvpathf = os.path.join(path, "datacv.csv")
if os.path.exists(cvpathf):
CV = np.loadtxt(cvpathf)
# load the Mu values
Mu = None
mupathf = os.path.join(path, "mu.csv")
if os.path.exists(mupathf):
Mu = np.loadtxt(mupathf)
# load the knee indices
Ik = None
kpathf = os.path.join(path, "muid.csv")
if os.path.exists(kpathf):
Ik = np.loadtxt(kpathf, dtype = int)
return CV, Mu, Ik
def decide_size_color(CV, Mu, Ik):
r"""
Decide point-size and color from `CV`, `Mu` and `Ik`
"""
# decide point-size
S = 1
if Mu is not None and Ik is not None:
S = resize_by_tradeoff(Mu, k=Ik)
# decide colors
# C = default_color(F.shape[0], alpha=0.5)
M = np.mean(F, axis=0)
C, _ = color_by_dist(F, M)
# if CV is available, use CV to color
if CV is not None:
C = color_by_cv(CV)
# enhance knee points
if Ik is not None:
C = enhance_color(C, Ik)
return S, C
Now do a scatter plot with appropriate colorcodings and markings¶
In [7]:
%matplotlib notebook
sys.path.append('../../')
from vis.plotting import scatter
from vis.plotting.scatter import camera_angles
pf,dim = 'dtlz2-nbi', '5d'
fullpathf = "../../data/{0:s}/{1:s}/dataf.csv".format(pf, dim)
if os.path.exists(fullpathf):
path, filenamef = os.path.split(fullpathf)
dirs = path.split('/')
frontname = dirs[-2]
# load the front
F = np.loadtxt(fullpathf, delimiter=',')
print(fullpathf, F.shape, dirs, frontname)
CV, Mu, Ik = load_props(path)
S,C = decide_size_color(CV, Mu, Ik)
pdffigpath = os.path.join(path, "scatter3d.pdf")
svgfigpath = os.path.join(path, "scatter3d.svg")
with plt.rc_context({"text.usetex": True, "font.size": 12}):
scatter.plot(F, ax=Axes3D(plt.figure()), s=S, c=C, euler=(45,25))#camera_angles[pf][dim])
plt.subplots_adjust(top=0.01, bottom=0, right=0.01, left=0, hspace=0, wspace=0)
plt.margins(0,0,0)
# save the fig
plt.savefig(pdffigpath, bbox_inches='tight', pad_inches=0, dpi=150)
plt.savefig(svgfigpath, bbox_inches='tight', pad_inches=0, dpi=150, format='svg')
plt.show()
Do paletteviz original¶
In [8]:
%matplotlib notebook
sys.path.append('../../')
from vis.plotting import paletteviz
from vis.plotting.paletteviz import camera_angles_star
pf,dim = 'dtlz2-nbi', '5d'
fullpathf = "../../data/{0:s}/{1:s}/dataf.csv".format(pf, dim)
if os.path.exists(fullpathf):
path, filenamef = os.path.split(fullpathf)
dirs = path.split('/')
frontname = dirs[-2]
# load the front
F = np.loadtxt(fullpathf, delimiter=',')
print(fullpathf, F.shape, dirs, frontname)
CV, Mu, Ik = load_props(path)
S,C = decide_size_color(CV, Mu, Ik)
pdffigpath = os.path.join(path, "palette-starviz.pdf")
svgfigpath = os.path.join(path, "palette-starviz.svg")
depth_contour_path = os.path.join(path, "depth-cont-cvhull.csv")
with plt.rc_context({"text.usetex": True, "font.size": 12}):
paletteviz.plot(F, ax=Axes3D(plt.figure()), depth_contours=depth_contour_path, mode='star', \
n_partitions=4, s=S, c=C, verbose=True, euler=(-40,15)) #camera_angles_star[pf][dim])
# try to remove white space as much as possible
plt.subplots_adjust(top=0.01, bottom=0, right=0.01, left=0, hspace=0, wspace=0)
plt.margins(0,0,0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.gca().zaxis.set_major_locator(plt.NullLocator())
# save the fig
plt.savefig(pdffigpath, bbox_inches='tight', dpi=150, pad_inches=0)
plt.savefig(svgfigpath, bbox_inches='tight', dpi=150, pad_inches=0, format='svg')
# show
plt.show()
Fig1: Plot 1000 points using PaletteViz in four levels.¶
Show point $f^{(0)}$ on the plot with a bright color (say, red or something, which is not in the paletteviz). This one should plotted in way so that all points are nicely visible.
In [9]:
%matplotlib notebook
import matplotlib.colors as mc
sys.path.append('../../')
from vis.plotting import scatter
from vis.plotting.scatter import camera_angles
from vis.plotting.utils import default_color, enhance_color
pf,dim = 'dtlz2-nbi', '5d'
fullpathf = "../../data/{0:s}/{1:s}/dataf.csv".format(pf, dim)
if os.path.exists(fullpathf):
path, filenamef = os.path.split(fullpathf)
dirs = path.split('/')
frontname = dirs[-2]
# load the front
F = np.loadtxt(fullpathf, delimiter=',')
print(fullpathf, F.shape, dirs, frontname)
CV, Mu, Ik = load_props(path)
S,C = decide_size_color(CV, Mu, Ik)
# change the color and size according to the requirements
C = default_color(F.shape[0], c=mc.TABLEAU_COLORS['tab:gray'], alpha=0.5)
if0 = np.array([0])
C = enhance_color(C, if0)
S[if0] = S[if0] * 6.0
pdffigpath = os.path.join(path, "scatter3d-fig1.pdf")
svgfigpath = os.path.join(path, "scatter3d-fig1.svg")
with plt.rc_context({"text.usetex": True, "font.size": 12}):
# plot
ax = Axes3D(plt.figure())
scatter.plot(F, ax=ax, s=S, c=C, euler=(45,25)) #camera_angles[pf][dim])
# mark point
ax.text(F[if0,0][0], F[if0,1][0], z=F[if0,2][0], s=r'$\mathbf{f^{{(0)}}}$', \
ha='right', va='bottom', fontsize='large')
# remove white space
plt.subplots_adjust(top=0.01, bottom=0, right=0.01, left=0, hspace=0, wspace=0)
plt.margins(0,0,0)
# save the fig
plt.savefig(pdffigpath, bbox_inches='tight', pad_inches=0, dpi=150)
plt.savefig(svgfigpath, bbox_inches='tight', pad_inches=0, dpi=150, format='svg')
# show
plt.show()
In [10]:
%matplotlib notebook
import matplotlib.colors as mc
sys.path.append('../../')
from vis.plotting import paletteviz
from vis.plotting.paletteviz import camera_angles_star
from vis.plotting.utils import default_color, enhance_color
pf,dim = 'dtlz2-nbi', '5d'
fullpathf = "../../data/{0:s}/{1:s}/dataf.csv".format(pf, dim)
if os.path.exists(fullpathf):
path, filenamef = os.path.split(fullpathf)
dirs = path.split('/')
frontname = dirs[-2]
# load the front
F = np.loadtxt(fullpathf, delimiter=',')
print(fullpathf, F.shape, dirs, frontname)
CV, Mu, Ik = load_props(path)
S,C = decide_size_color(CV, Mu, Ik)
# change the color and size according to the requirements
C = default_color(F.shape[0], c=mc.TABLEAU_COLORS['tab:gray'], alpha=0.5)
if0 = np.array([0])
C = enhance_color(C, if0)
S[if0] = S[if0] * 6.0
pdffigpath = os.path.join(path, "palette-starviz-fig1.pdf")
svgfigpath = os.path.join(path, "palette-starviz-fig1.svg")
depth_contour_path = os.path.join(path, "depth-cont-cvhull.csv")
with plt.rc_context({"text.usetex": True, "font.size": 12}):
# plot
ax = Axes3D(plt.figure())
_,P = paletteviz.plot(F, ax=ax, depth_contours=depth_contour_path, mode='star', \
n_partitions=4, s=S, c=C, verbose=True, euler=(-40,15)) # camera_angles_star[pf][dim])
# mark points
ax.text(P[if0,0][0], P[if0,1][0], z=P[if0,2][0], s=r'$\mathbf{f^{{(0)}}}$', \
ha='right', va='bottom', fontsize='large')
# try to remove white space as much as possible
plt.subplots_adjust(top=0.01, bottom=0, right=0.01, left=0, hspace=0, wspace=0)
plt.margins(0,0,0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.gca().zaxis.set_major_locator(plt.NullLocator())
# save the fig
plt.savefig(pdffigpath, bbox_inches='tight', dpi=150, pad_inches=0)
plt.savefig(svgfigpath, bbox_inches='tight', dpi=150, pad_inches=0, format='svg')
# show
plt.show()
Fig 2: On the another PaletteViz with same 1000 points, plot $f^{(1)}$ and join with an arrow from $f^{(0)}$.¶
Mark three equidistant intermediate points on the line:
- $p_1 = f^{(0)} + 0.25 (f^{(1)} - f^{(0)})$
- $p_2 = f^{(0)} + 0.50 (f^{(1)} - f^{(0)})$
- $p_3 = f^{(0)} + 0.75 (f^{(1)} - f^{(0)})$
Then, mark $z^{(1)}$ to $z^{(3)}$ points with another bright color. Join $z^{(1)}$ with the first intermediate point, and so on, to indicate that $z^{(1)}$ is obtained from p1 and so on. These points may be at different levels (if not, let me know, I need to add more points). Here, focus on near all these points.
In [11]:
from scipy.spatial import cKDTree
# two starting point indices
print(F.shape)
if0f1 = np.array([0, 300])
f0 = F[if0f1[0],:]
f1 = F[if0f1[1],:]
# three intermediate points
p1 = f0 + 0.25 * (f1 - f0)
p2 = f0 + 0.50 * (f1 - f0)
p3 = f0 + 0.75 * (f1 - f0)
# build a tree
tree = cKDTree(F)
# get the intermediate point indices
_,ip1 = tree.query(p1, k=1)
_,ip2 = tree.query(p2, k=1)
_,ip3 = tree.query(p3, k=1)
if0f1 = np.array([if0f1[0], ip1, ip2, ip3, if0f1[1]])
print(if0f1)
In [12]:
%matplotlib notebook
sys.path.append('../../')
from vis.plotting import scatter
from vis.plotting.scatter import camera_angles
from vis.plotting.utils import default_color, enhance_color, Arrow3D
pf,dim = 'dtlz2-nbi', '5d'
fullpathf = "../../data/{0:s}/{1:s}/dataf.csv".format(pf, dim)
if os.path.exists(fullpathf):
path, filenamef = os.path.split(fullpathf)
dirs = path.split('/')
frontname = dirs[-2]
# load the front
F = np.loadtxt(fullpathf, delimiter=',')
print(fullpathf, F.shape, dirs, frontname)
CV, Mu, Ik = load_props(path)
S,C = decide_size_color(CV, Mu, Ik)
# change the color and size according to the requirements
C = default_color(F.shape[0], c=mc.TABLEAU_COLORS['tab:gray'], alpha=0.5)
# enhance intermediate points
C = enhance_color(C, if0f1, alpha=1.0)
S[if0f1] = S[if0f1] * 6.0
# point labels
labels = [r'$\mathbf{f^{{(0)}}}$', r'$\mathbf{p_{{1}}}$', r'$\mathbf{p_{{2}}}$', \
r'$\mathbf{p_{{3}}}$', r'$\mathbf{f^{{(1)}}}$']
pdffigpath = os.path.join(path, "scatter3d-fig2.pdf")
svgfigpath = os.path.join(path, "scatter3d-fig2.svg")
with plt.rc_context({"text.usetex": True, "font.size": 12}):
# plot
ax = Axes3D(plt.figure())
scatter.plot(F, ax=ax, s=S, c=C, euler=(45,25))#camera_angles[pf][dim])
# mark points
for i in range(if0f1.shape[0]):
ax.text(F[if0f1[i],0]-0.025, F[if0f1[i],1]-0.025, z=F[if0f1[i],2], s=labels[i], \
ha='left', va='bottom', fontsize='large')
# draw arrows
for i in range(if0f1.shape[0]-1):
a = Arrow3D(\
[F[if0f1[i],0], F[if0f1[i+1],0]], \
[F[if0f1[i],1], F[if0f1[i+1],1]], \
[F[if0f1[i],2], F[if0f1[i+1],2]], \
mutation_scale = 10, lw = 0.75, \
arrowstyle = "-|>", color = 'black')
ax.add_artist(a)
# remove white spaces
plt.subplots_adjust(top=0.01, bottom=0, right=0.01, left=0, hspace=0, wspace=0)
plt.margins(0,0,0)
# save the fig
plt.savefig(pdffigpath, bbox_inches='tight', pad_inches=0, dpi=150)
plt.savefig(svgfigpath, bbox_inches='tight', pad_inches=0, dpi=150, format='svg')
# show
plt.show()
In [13]:
%matplotlib notebook
import matplotlib.colors as mc
sys.path.append('../../')
from vis.plotting import paletteviz
from vis.plotting.paletteviz import camera_angles_star
from vis.plotting.utils import default_color, enhance_color
pf,dim = 'dtlz2-nbi', '5d'
fullpathf = "../../data/{0:s}/{1:s}/dataf.csv".format(pf, dim)
if os.path.exists(fullpathf):
path, filenamef = os.path.split(fullpathf)
dirs = path.split('/')
frontname = dirs[-2]
# load the front
F = np.loadtxt(fullpathf, delimiter=',')
print(fullpathf, F.shape, dirs, frontname)
CV, Mu, Ik = load_props(path)
S,C = decide_size_color(CV, Mu, Ik)
# change the color and size according to the requirements
C = default_color(F.shape[0], c=mc.TABLEAU_COLORS['tab:gray'], alpha=0.5)
# enhance intermediate points
C = enhance_color(C, if0f1, alpha=1.0)
S[if0f1] = S[if0f1] * 6.0
# point labels
labels = [r'$\mathbf{f^{{(0)}}}$', r'$\mathbf{p_{{1}}}$', r'$\mathbf{p_{{2}}}$', \
r'$\mathbf{p_{{3}}}$', r'$\mathbf{f^{{(1)}}}$']
# label pos
lpos = [['left', 'bottom'], ['left', 'bottom'], ['left', 'bottom'], ['right', 'bottom'], ['left', 'bottom']]
pdffigpath = os.path.join(path, "palette-starviz-fig2.pdf")
svgfigpath = os.path.join(path, "palette-starviz-fig2.svg")
depth_contour_path = os.path.join(path, "depth-cont-cvhull.csv")
with plt.rc_context({"text.usetex": True, "font.size": 12}):
ax = Axes3D(plt.figure())
_,P = paletteviz.plot(F, ax=ax, depth_contours=depth_contour_path, mode='star', \
n_partitions=4, s=S, c=C, verbose=True, euler=(-40,15)) # camera_angles_star[pf][dim])
# mark points
for i in range(if0f1.shape[0]):
ax.text(P[if0f1[i],0]+0.025, P[if0f1[i],1]+0.025, z=P[if0f1[i],2], s=labels[i], \
ha=lpos[i][0], va=lpos[i][1], fontsize='large')
# draw arrows
for i in range(len(if0f1)-1):
a = Arrow3D(\
[P[if0f1[i],0], P[if0f1[i+1],0]], \
[P[if0f1[i],1], P[if0f1[i+1],1]], \
[P[if0f1[i],2], P[if0f1[i+1],2]], \
mutation_scale = 10, lw = 0.75, \
arrowstyle = "-|>", color = 'black')
ax.add_artist(a)
# try to remove white space as much as possible
plt.subplots_adjust(top=0.01, bottom=0, right=0.01, left=0, hspace=0, wspace=0)
plt.margins(0,0,0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.gca().zaxis.set_major_locator(plt.NullLocator())
# save the fig
plt.savefig(pdffigpath, bbox_inches='tight', dpi=150, pad_inches=0)
plt.savefig(svgfigpath, bbox_inches='tight', dpi=150, pad_inches=0, format='svg')
# show
plt.show()
Fig 3: On the another PaletteViz with same 1000 points, plot f^(2) and join with an arrow from f^(1).¶
Then, mark intermediate points
- $p_1 = f^{(1)} + 0.3333 (f^{(2)} - f^{(1)})$
- $p_2 = f^{(1)} + 0.6667 (f^{(2)} - f^{(1)})$
- $p_2 = f^{(1)} + 0.9999 (f^{(2)} - f^{(1)})$
and $z^{(1)}$ to $z^{(2)}$ points with another bright color. Join them. Then, plot the third iteration points $f^{(3)}$, join with $f^{(2)}$, and then mark two intermediate points $p_1$ and $p_2$ and their respective points $z^{(1)}$ and $z^{(2)}$ and join. Here also focus on the points.
In [14]:
from scipy.spatial import cKDTree
# two starting point indices
print(F.shape)
if0f1 = np.array([0, 300])
f0 = F[if0f1[0],:]
f1 = F[if0f1[1],:]
# three intermediate points
p1 = f0 + 0.3333 * (f1 - f0)
p2 = f0 + 0.6667 * (f1 - f0)
# build a tree
tree = cKDTree(F)
# get the intermediate point indices
_,ip1 = tree.query(p1, k=1)
_,ip2 = tree.query(p2, k=1)
if0f1 = np.array([if0f1[0], ip1, ip2, if0f1[1]])
print(if0f1)
In [15]:
%matplotlib notebook
sys.path.append('../../')
from vis.plotting import scatter
from vis.plotting.scatter import camera_angles
from vis.plotting.utils import default_color, enhance_color, Arrow3D
pf,dim = 'dtlz2-nbi', '5d'
fullpathf = "../../data/{0:s}/{1:s}/dataf.csv".format(pf, dim)
if os.path.exists(fullpathf):
path, filenamef = os.path.split(fullpathf)
dirs = path.split('/')
frontname = dirs[-2]
# load the front
F = np.loadtxt(fullpathf, delimiter=',')
print(fullpathf, F.shape, dirs, frontname)
CV, Mu, Ik = load_props(path)
S,C = decide_size_color(CV, Mu, Ik)
# change the color and size according to the requirements
C = default_color(F.shape[0], c=mc.TABLEAU_COLORS['tab:gray'], alpha=0.5)
# enhance intermediate points
C = enhance_color(C, if0f1, alpha=1.0)
S[if0f1] = S[if0f1] * 6.0
# point labels
labels = [r'$\mathbf{f^{{(0)}}}$', r'$\mathbf{p_{{1}}}$', r'$\mathbf{p_{{2}}}$', r'$\mathbf{f^{{(1)}}}$']
pdffigpath = os.path.join(path, "scatter3d-fig3.pdf")
svgfigpath = os.path.join(path, "scatter3d-fig3.svg")
with plt.rc_context({"text.usetex": True, "font.size": 12}):
# plot
ax = Axes3D(plt.figure())
scatter.plot(F, ax=ax, s=S, c=C, euler=(45,25))#camera_angles[pf][dim])
# mark points
for i in range(if0f1.shape[0]):
ax.text(F[if0f1[i],0]-0.025, F[if0f1[i],1]-0.025, z=F[if0f1[i],2], s=labels[i], \
ha='left', va='bottom', fontsize='large')
# draw arrows
for i in range(if0f1.shape[0]-1):
a = Arrow3D(\
[F[if0f1[i],0], F[if0f1[i+1],0]], \
[F[if0f1[i],1], F[if0f1[i+1],1]], \
[F[if0f1[i],2], F[if0f1[i+1],2]], \
mutation_scale = 10, lw = 0.75, \
arrowstyle = "-|>", color = 'black')
ax.add_artist(a)
# remove white space
plt.subplots_adjust(top=0.01, bottom=0, right=0.01, left=0, hspace=0, wspace=0)
plt.margins(0,0,0)
# save the fig
plt.savefig(pdffigpath, bbox_inches='tight', pad_inches=0, dpi=150)
plt.savefig(svgfigpath, bbox_inches='tight', pad_inches=0, dpi=150, format='svg')
# show
plt.show()
In [16]:
%matplotlib notebook
import matplotlib.colors as mc
sys.path.append('../../')
from vis.plotting import paletteviz
from vis.plotting.paletteviz import camera_angles_star
from vis.plotting.utils import default_color, enhance_color
pf,dim = 'dtlz2-nbi', '5d'
fullpathf = "../../data/{0:s}/{1:s}/dataf.csv".format(pf, dim)
if os.path.exists(fullpathf):
path, filenamef = os.path.split(fullpathf)
dirs = path.split('/')
frontname = dirs[-2]
# load the front
F = np.loadtxt(fullpathf, delimiter=',')
print(fullpathf, F.shape, dirs, frontname)
CV, Mu, Ik = load_props(path)
S,C = decide_size_color(CV, Mu, Ik)
# change the color and size according to the requirements
C = default_color(F.shape[0], c=mc.TABLEAU_COLORS['tab:gray'], alpha=0.5)
# enhance intermediate points
C = enhance_color(C, if0f1, alpha=1.0)
S[if0f1] = S[if0f1] * 6.0
# point labels
labels = [r'$\mathbf{f^{{(0)}}}$', r'$\mathbf{p_{{1}}}$', r'$\mathbf{p_{{2}}}$', r'$\mathbf{f^{{(1)}}}$']
# label pos
lpos = [['left', 'bottom'], ['right', 'bottom'], ['right', 'bottom'], ['left', 'bottom']]
pdffigpath = os.path.join(path, "palette-starviz-fig3.pdf")
svgfigpath = os.path.join(path, "palette-starviz-fig3.svg")
depth_contour_path = os.path.join(path, "depth-cont-cvhull.csv")
with plt.rc_context({"text.usetex": True, "font.size": 12}):
# plot
ax = Axes3D(plt.figure())
_,P = paletteviz.plot(F, ax=ax, depth_contours=depth_contour_path, mode='star', \
n_partitions=4, s=S, c=C, verbose=True, euler=(-40,15)) # camera_angles_star[pf][dim])
# mark points
for i in range(if0f1.shape[0]):
ax.text(P[if0f1[i],0]-0.025, P[if0f1[i],1]-0.025, z=P[if0f1[i],2], s=labels[i], \
ha=lpos[i][0], va=lpos[i][1], fontsize='large')
# draw arrows
for i in range(if0f1.shape[0]-1):
a = Arrow3D(\
[P[if0f1[i],0], P[if0f1[i+1],0]], \
[P[if0f1[i],1], P[if0f1[i+1],1]], \
[P[if0f1[i],2], P[if0f1[i+1],2]], \
mutation_scale = 10, lw = 0.75, \
arrowstyle = "-|>", color = 'black')
ax.add_artist(a)
# try to remove white space as much as possible
plt.subplots_adjust(top=0.01, bottom=0, right=0.01, left=0, hspace=0, wspace=0)
plt.margins(0,0,0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.gca().zaxis.set_major_locator(plt.NullLocator())
# save the fig
plt.savefig(pdffigpath, bbox_inches='tight', dpi=150, pad_inches=0)
plt.savefig(svgfigpath, bbox_inches='tight', dpi=150, pad_inches=0, format='svg')
# show
plt.show()
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